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The applications of process mining, which is a business process management technique, in the field of healthcare are increasing day by day. In process mining, it is possible to analyze the process for three main purposes: discovery of the process, compliance control and process improvement, based on event logs recorded in information systems. Transforming patient-based healthcare processes data into process and event-based event logs is the first step of a process mining project in order to apply process mining techniques to human-centered, distributed, complex and multidisciplinary healthcare processes and to improve the quality of health care services. The process model discovered in multi-perspective process mining is expanded from different perspectives such as control flow, organizational, data, time and function, and the discovered process becomes more understandable. In this study, and in order to apply multi-perspective process mining, a method is proposed to convert the healthcare processes data recorded in hospital information systems into event logs. Data transformation method consists of six steps: data collection and data privacy, data integration, data conversion, data preprocessing, feature selection and extraction, and multi-perspective process mining analysis. The proposed method was validated by a case study by transforming the surgery process data of a university hospital in Turkey into an event log. The process discovery algorithm was applied to the surgery process data of the case study and the actual process was discovered, and the applicability of the data transformation method was shown on the real data. With the guiding feature of the method for healthcare professionals, it is expected to contribute to the applications of multi-perspective process mining in the field of healthcare in Turkey. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions. [ABSTRACT FROM AUTHOR]

Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. [ABSTRACT FROM AUTHOR]

Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models. [ABSTRACT FROM AUTHOR]

Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs. [ABSTRACT FROM AUTHOR]

Background: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. Methods: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. Results: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. Conclusions: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment. [ABSTRACT FROM AUTHOR]

The absence of a Standard Operating Procedure (SOP) can lead to many problems in operations within organisations. Process mining techniques can discover process models that reflect the actual behaviour of the process implementations by using event logs extracted from information systems. However, the process models discovered by process mining often have too many variations and deviations when compared to the actual SOPs of the processes. This study attempted to compare three prediction methods in finding a process model from process mining that has the closest properties to the actual SOP. The compared methods are Receiver Operating Characteristics (ROC), the four quality dimensions, and similarity measures for structural and behavioural similarities. For the experiment, we designed a synthetic SOP that served as a ground truth for evaluating the performance of the three prediction methods in this study. We used a synthetic event log extracted from a dummy information system we particularly built for this study to test the methods. This study’s results can be useful, e.g. for auditors to save a lot of time from conducting extensive surveys when SOPs are not readily available. [ABSTRACT FROM AUTHOR]

This paper discusses a methodology to improve the prevention processes of chronic diseases such as diabetes and strokes. The research motivation is to find a new methodological approach to design advanced Diagnostic and Therapeutic Care Pathways (PDTAs) based on the prediction of chronic disease using telemedicine technologies and machine learning (ML) data processing techniques. The aim is to decrease health risk and avoid hospitalizations through prevention. The proposed method defines a Process Mining Organization (PMO) model, managing risks using a PDTA structured to prevent chronic risk. Specifically, the data analysis is focused on stroke risk. First, we applied and compared the Random Forest (RF) and Gradient Boosted Trees (GBT) supervised algorithms to predict stroke risk, and then, the Fuzzy c-Means unsupervised algorithm to cluster information on the predicted results. The application of the proposed approach is able to increase the efficiency of healthcare human resources and drastically decrease care costs. [ABSTRACT FROM AUTHOR]

This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success. [ABSTRACT FROM AUTHOR]

Generally, these days people live longer but often with increased impairment and disabilities; therefore, they can benefit from assistive technologies. In this paper, we focus on the completion of activities of daily living (ADLs) by such patients, using so-called Smart Homes and Sensor Technology to collect data, and provide a suitable analysis to support the management of these conditions. The activities here are cast as states of a Markov-type process, while changes of state are indicated by sensor activations. This facilitates the extraction of key performance indicators (KPIs) in Smart Homes, e.g., the duration of an important activity, as well as the identification of anomalies in such transitions and durations. The use of semi-Markov models for such a scenario is described, where the state durations are represented by mixed gamma models. This approach is illustrated and evaluated using a publicly available Smart Home dataset comprising an event log of sensor activations, together with an annotated record of the actual activities. Results indicate that the methodology is well-suited to such scenarios. [ABSTRACT FROM AUTHOR]

Event logs are the main source for business process mining techniques. However, readily available logs are produced only by part of the existing systems, which may not always be part of an investigated environment. Furthermore, logs that are created by a given information system may reflect only parts of the full process, while other parts may span additional systems. We suggest that data generated by communication network traffic that is associated with business processes can fill this gap, both in availability and in span. However, traffic data are technically oriented and noisy, and there is a huge conceptual gap between these data and business meaningful event logs. Considering the above, we set the following aims. First, to assess whether the gap between technical-level traffic data and conceptual-level business activities can be bridged. Once this is established, to automatically recognize business activities within network traffic data, considering that these data hold interleaving activities that are performed in parallel. To address the first aim, we develop a conceptual model of traffic behavior that corresponds to a business activity. We use simulated traffic data annotated by the originating activity and perform an iterative process of abstracting and filtering the data, along with the application of process discovery. As a result, we obtained distinct process models for specific activity types and a generic higher-level model of traffic behavior in a business activity. To address the second aim, relying on the insights gained from the conceptual models, we propose a method utilizing sequence learning to identify activity types, and their boundaries (start and end) within network traffic data. Evaluation shows that the proposed approach has a high precision and recall in classifying packets by activities, even while these activities are performed in parallel to each other and their data are interleaved. [ABSTRACT FROM AUTHOR]

Business process management (BPM) is a well-established discipline comprising a set of principles, methods, techniques, and tools to continuously improve the performance of business processes. Traditionally, most BPM decisions and activities are undertaken by business stakeholders based on manual data collection and analysis techniques. This is time-consuming and potentially leads to suboptimal decisions, as only a restricted subset of data and options are considered. Over the past decades, a rich set of data-driven techniques has emerged to support and automate various activities and decisions across the BPM lifecycle, particularly within the process mining field. More recently, the uptake of artificial intelligence (AI) methods for BPM has led to a range of approaches for proactive business process monitoring. Given their common data requirements and overlapping goals, process mining and AI-driven approaches to business process optimization are converging. This convergence is leading to a promising emerging concept, which we call (AI-)augmented process execution: a collection of data analytics and artificial intelligence methods for continuous and automated improvement and adaptation of business processes. This article gives an outline of research at the intersection between process mining and AI-driven process optimization, classifies the researched techniques based on their scope and objectives, and positions augmented process execution as an additional layer on top of this stack. [ABSTRACT FROM AUTHOR]

Business process management, design, and analysis is mostly centered around a process model, which depicts the behavior of a process case (instance). As a result, behavior that associates several cases together has received less attention. Yet, it is important to understand and track associations among cases, as they bear substantial consequences for compliance with regulations, root cause analysis of performance issues, exception handling, and prediction. This paper presents a framework of cross-case association patterns, categorized as intended association patterns and contextual association patterns. It further conceptualizes two example patterns—one for each category, and proposes techniques for detecting these patterns in an event log. The "split-case" workaround is an example of a pattern in the intended association category, and its proposed detection method exemplifies how patterns in this category can be approached. The patterns of a shared entity and a shared resource are contextual association patterns, which we propose to detect by means of hidden concept drifts. Evaluation of the two detection approaches is reported, using simulated logs for assessing their internal validity as well as real-life ones for exploring their external validity. [ABSTRACT FROM AUTHOR]

Using the right tools to support decision-making for managers is very important. Today, using mathematical methods to support managers in making decisions is prevalent. Data envelopment analysis is one of the mathematical methods used in the mentioned field. In recent years, researchers in this field have developed data envelopment analysis methods to a great extent. Many data organizations use to evaluate performance and other purposes are imprecise and uncertain. To solve this problem, one should use methods whose results are reliable. Stochastic, fuzzy-stochastic, fuzzy, robust, etc. models can be mentioned among these methods. Among non-deterministic methods, stochastic models are of particular importance. This article only reviews the existing stochastic methods for dealing with non-deterministic data. We have divided stochastic methods into four categories to understand the subject better. Also, the fit between the models and the data has been reviewed in the articles. The process mining method is suggested to determine the appropriate model for the available data. Finally, the weaknesses of some previous models have been introduced. Suggestions have been made to fix these weaknesses. The current research results provide researchers in this field with appropriate information regarding stochastic data envelopment analysis. [ABSTRACT FROM AUTHOR]

Process variants usually exhibit a high degree of internal heterogeneity, in the sense that the executions of the process differ widely from each other due to contextual factors, human factors, or deliberate business decisions. Understanding differences among process variants helps analysts and managers to make informed decisions as to how to standardise or otherwise improve a business process. Existing process variant mining approaches typically fall short in full supporting semantic process variability mining, especially rarely taking activity behaviour relationships and trace context semantic into consideration. Here, we propose a semantic process variant discovery method, aimed at solving the difficulty of distinguishing similar-but-different behaviours directly from event logs. More specifically, we adapt concepts of benchmark logs and trace context tree to formalise context semantic of event log, to classify benchmark logs into several parts, thereby the clustered trace cohorts are mapped to discover the configurable process variants. In the experimental part, some performance metrics of the proposed method are evaluated and calculated by real-world event logs, supporting the usefulness of the proposed method. The experimental results show that the proposed method is able to distinguish similar-but-different behaviours and is superior to the characteristic trace clustering method using conventional neural networks. [ABSTRACT FROM AUTHOR]

The learning process in online lectures through the Learning Management System (LMS) will produce a learning flow according to the event log. Assessment in a group of parallel classes is expected to produce the same assessment point of view based on the semester lesson plan. However, it does not rule out the implementation of each class to produce unequal fairness. Some of the factors considered to influence the assessment in the classroom include the flow of learning, different lecturers, class composition, time and type of assessment, and student attendance. The implementation of process mining in fairness assessment is used to determine the extent to which the learning flow plays a role in the assessment of ten parallel classes, including international classes. Moreover, a decision tree algorithm will also be applied to determine the root cause of the student assessment analysis based on the causal factors. As a result, there are three variables that have effects on student graduation and assessment, i.e attendance, class and gender. Variable lecturer does not have much impact on the assessment, but has an influence on the learning flow. [ABSTRACT FROM AUTHOR]

The decline in the marketing volume of Rabobank Group ICT is a serious incident as it can hinder the implementation of an increasing number of software releases for business development. The Service Desk Agent records the activities that occur to find out the problems experienced in the form of an event log. Process mining can be used to generate process model visualizations based on event logs to explicitly monitor the business. Fuzzy Miner and Heuristic Miner algorithms can be used to handle complex event logs. In this study, an analysis of the Rabobank Group ICT incident was carried out with process mining using the Fuzzy Miner and Heuristic Miner algorithms. Process mining is done by discovery, conformance, and enhancement. Based on the results of the study, it is known that the division of the work area is not good enough to cause a team to work on a lot of events while there are other teams that only work on one event. Therefore, it is necessary to have a clear and balanced division of domains and workloads so that incidents do not recur. [ABSTRACT FROM AUTHOR]

Through business process modeling, organizations in general have managed to optimize their processes, through a robust and automated process architecture. Similarly, through risk management, companies seek to ensure that the risks that can negatively affect the achievement of the organizations' strategic objectives are identified, analyzed, and adequately responded to. Based on the risk assessment, an organization will need to choose whether to accept the risk, mitigate the risk, or transfer the risk to the same or another process. When carried out effectively, risk management in business processes ensures that the organization's limited resources are prioritized to address in the most efficient way the problems that will affect them the most, and thus their continuity is achieved. and sustainability. This article explores questions associated with What is a business process, BPM? and What are the risks associated with business processes? How does the use of BPM modeling and process mining facilitate risk management at all stages of the process lifecycle? The most important contributions are related to the mapping of cycle risks to a functional and specific framework, and affect the life cycle in its entirety, which were analyzed through process mining. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the preprocessing of the raw data to selecting the best LSTM model. The effectiveness of the prediction method is evaluated through four real-life event logs that contain historical information on the execution of the processes of patient transfer orders between hospitals, sepsis care cases, billing of medical services, and patient care management. In the test stage, the LSTM model reached values of 0.98, 0.91, 0.85, and 0.81 in the accuracy metric, and in the evaluation of the prediction of the next event using the 10-fold cross-validation technique, values of 0.94, 0.88, 0.84, and 0.81 were obtained for the four previously mentioned event logs. In addition, the performance of the LSTM prediction model was evaluated with the precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) metrics, obtaining high scores very close to 1. The experimental results suggest that the proposed method achieves acceptable measures in predicting the next event regardless of whether an input event or a set of input events is used. [ABSTRACT FROM AUTHOR]

The World Health Organization has estimated that air pollution will be one of the most significant challenges related to the environment in the following years, and air quality monitoring and climate change mitigation actions have been promoted due to the Paris Agreement because of their impact on mortality risk. Thus, generating a methodology that supports experts in making decisions based on exposure data, identifying exposure-related activities, and proposing mitigation scenarios is essential. In this context, the emergence of Interactive Process Mining—a discipline that has progressed in the last years in healthcare—could help to develop a methodology based on human knowledge. For this reason, we propose a new methodology for a sequence-oriented sensitive analysis to identify the best activities and parameters to offer a mitigation policy. This methodology is innovative in the following points: i) we present in this paper the first application of Interactive Process Mining pollution personal exposure mitigation; ii) our solution reduces the computation cost and time of the traditional sensitive analysis; iii) the methodology is human-oriented in the sense that the process should be done with the environmental expert; and iv) our solution has been tested with synthetic data to explore the viability before the move to physical exposure measurements, taking the city of Valencia as the use case, and overcoming the difficulty of performing exposure measurements. This dataset has been generated with a model that considers the city of Valencia's demographic and epidemiological statistics. We have demonstrated that the assessments done using sequence-oriented sensitive analysis can identify target activities. The proposed scenarios can improve the initial KPIs—in the best scenario; we reduce the population exposure by 18% and the relative risk by 12%. Consequently, our proposal could be used with real data in future steps, becoming an innovative point for air pollution mitigation and environmental improvement. [ABSTRACT FROM AUTHOR]

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT. [ABSTRACT FROM AUTHOR]

Process models are important for supporting organizations in documenting, understanding and monitoring their business. When these process models become outdated, they need to be revised to accurately describe the new status quo of the processes in the organization. Process model repair techniques help at automatically revising the existing model from behavior traced in event logs. So far, such techniques have focused on identifying which parts of the model to change and how to change them, but they do not use knowledge from practitioners to inform the revision. As a consequence, fragments of the model may change in a way that defies existing regulations or represents outdated information that was wrongly considered from the event log. This paper uses concepts from theory revision to provide formal foundations for process model repair that exploits domain knowledge. Specifically, it conceptualizes (1) what are unchangeable fragments in the model and (2) the role that various traces in the event log should play when it comes to model repair. A scenario of use is presented that demonstrates the benefits of this conceptualization. The current state of existing process model repair techniques is compared against the proposed concepts. The results show that only two existing techniques partially consider the concepts presented in this paper for model repair. [ABSTRACT FROM AUTHOR]

Background: Many learners struggle to productively self‐regulate their learning. To support the learners' self‐regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL. To measure these processes, contemporary SRL researchers have largely utilized think aloud or trace data, however, not without challenges. Objectives: In this paper, we present the findings of a study that investigated how concurrent analysis and integration of think aloud and trace data could advance the measurement of SRL and assist in better understanding the mechanisms of SRL processes, especially those details that remain obscured by observing each data channel individually. Methods: We concurrently collected think aloud and trace data generated by 44 university students in a laboratory setting and analysed those data relative to the same timeline. Results: We found that the two data channels could be interchangeably used to measure SRL processes for only 17.18% of all the time segments identified in a learning task. Moreover, SRL processes for around 45% of all the time segments could be detected via either trace data or think aloud data. For another 27.17% of all the time segments, different SRL processes were detected in both data channels. Conclusions: Our results largely suggest that the two data collection methods can be used to complement each other in measuring SRL. In particular, we found that think aloud and trace data could provide different perspectives on SRL. The integration of the two methods further allowed us to reveal a more complex and more comprehensive temporal associations among SRL processes compared to using a single data collection method. In future research, the integrated measurement of SRL can be used to improve the detection of SRL processes and provide a fuller picture of SRL. Lay Description: What is already known about this topic: To support the learners' self‐regulated learning (SRL) and boost their achievement, it is essential to understand the cognitive and metacognitive processes that underlie SRL.To measure SRL processes, contemporary researchers have largely utilized think aloud or trace data, however, not without challenges.Think aloud and trace data should complement each other and, when analysed concurrently, can provide a more valid and fuller picture of SRL than when analysed separately, which is typically the case in the SRL research to date. What this paper adds: We proposed a novel alignment approach to triangulate the measurement of self‐regulated learning (SRL) based on trace and think aloud data.We defined integration rules to integrate the measurement results from trace data and think aloud, which helped to provide a fuller picture of SRL.The integrated results revealed a more complex, complete and comprehensive SRL process map compared to using a single method. Implications for practice and/or policy: First, our results suggest that using a single measurement method can often reveal SRL processes only partially.Second, our findings indicated that the integration of the two measurement methods could not address all their methodological shortcomings and more research is needed towards new integrative approaches that can further reduce the number of misaligned results.In future research, the integrated measurement of SRL can be used to improve the detection of SRL processes and provide a fuller picture of SRL.More specifically, the integrated measurement of SRL can be used in the future to better (1) test the effects of instructional SRL interventions, for example, scaffolding; and (2) evaluate how learners use specific learning tools. [ABSTRACT FROM AUTHOR]

The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called "spaghetti-like" process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case. [ABSTRACT FROM AUTHOR]

Mine safety evaluation (MSE) is a complicated engineering problem that may contain the truth, falsity, and indeterminacy information. In the process of mine safety evaluation, the linguistic neutrosophic multivalued information can conveniently express the truth, falsity, and indeterminacy linguistic values and satisfy the thinking and expression habits of decision makers. Then, existing probabilistic linguistic neutrosophic decision-making methods are difficult to reveal the real probability values in several linguistic evaluation values. To reasonably simplify the expression and operation of the linguistic neutrosophic multivalued information and overcome the defects of the probability methods, the aim of this study is to propose a safety evaluation method of mines using correlation coefficients of consistency linguistic neutrosophic sets (CLNSs) in the setting of linguistic neutrosophic multivalued sets (LNMVSs). To do so, this study first defines LNMVS based on truth, falsity, and indeterminacy linguistic multivalued sequences with identical and/or different linguistic values, and then proposes a transformation method from LNMVS to CLNS based on the average values and consistency degrees (complement of standard deviation) of the linguistic multivalued sequences to reasonably simplify the information expression and operations of LNMVSs. Next, correlation coefficients between CLNSs and their MSE method are developed to solve MSE problems. By an actual example about the safety assessment of metal mines and relative comparative analysis, the evaluated results reveal that the proposed MSE method can reasonably and effectively deal with safety evaluation problems of the mines in the setting of LNMVSs. The proposed method can not only reflect the mean and consistency information of multiple language evaluation values and overcome the defect of unreasonable information generated by the probabilistic language method in small evaluation data, but also make evaluation results more effective and credible. [ABSTRACT FROM AUTHOR]

Purpose: Process mining connects the disciplines of data mining and machine learning to business process management techniques. A business process is a series of independent and interdependent activities that transform inputs (data, materials, etc.) using one or more resources (such as time, employees, and money). It utilizes the necessary outputs. It is possible to examine the actual behavior of organizations, including the performance of individuals, departments, and resources, using process analysis techniques. The results of the process analysis, which typically includes the organization's business process models, can be compared to the organization's documents and requirements. Thus, processes will be able to be compared, reviewed, monitored, and enhanced. Process mining methods operate based on event logs stored in information systems. Using process mining without high-quality input data will not result in accurate conclusions about an organization's business processes. In recent years, researchers have focused on the evaluation and enhancement of the quality of input data using process mining techniques. The objective of this study is to identify and categorize the most significant data quality issues, as well as recognize the approaches proposed to address this challenge in process mining. Methods: This research employs a systematic review with the intent of analyzing all valid evidence in order to answer the research questions. This study investigates 102 academic studies published between 2007 and 2021, including conference papers, journal articles, and theses. Towards this end, a systematic three-part research methodology was employed. In the first section, which included the research definition, the research field was defined first, followed by the research objectives and queries. In the concluding step of this section, the research's scope is defined. In the second section, the research methodology and entry criteria for the studies discovered during the search for scientific resources are defined. Finally, the identified studies are evaluated in terms of their citations and classified. In the third section, which is devoted to the evaluation of the research, the concluding research of the study is conducted, and then, based on the investigation of the preceding studies, the findings and conclusions are determined. Important data and evidence were extracted from the collated research, allowing for the creation of the necessary tables and graphs. Findings: In recent years, researchers have paid more attention to data quality challenges in the process mining, according to the findings of recent research. In 2019 and 2020, the greatest number of studies will have been published. It was also discovered that the majority of articles were published in three scientific databases, namely Springer, IEEE, and Elsevier. 51% of the studies examined were presented at prestigious conferences. 36% of the studies were published in prestigious scientific journals, while the remaining 13% were represented in dissertations and university reports. The study of the selected articles revealed that 20 data quality issues that can arise in the input data have been investigated in the literature. These challenges have been categorized into five levels: trace, event, case, activity, and timestamps, and four foundational approaches have been identified that have been used to evaluate and resolve data quality challenges in the mining process. 1) data quality frameworks 2) preprocessing 3) anomaly detection 4) repair. Our findings indicate that preprocessing techniques that seek to remove chaotic and infrequent behaviors from the event log have received more attention than other techniques. In addition, these results demonstrate that, in recent years, the discovery of anomalies and the reconstruction of missing events have become popular research topics within the field of process mining. Examining studies related to the field of data quality in the data mining process reveals an abundance of approaches and methods for addressing data quality challenges. Investigations revealed that the use of colorful Petri nets as a mathematical method has been considered in all selected research projects. Conclusions: The data needed for process mining methods can be obtained from various sources. One of the major advantages of process mining is that it is not limited to a specific type of system. Any workflow-based system, such as ticketing, resource management, databases, data warehouses, legacy systems, and even manually collected data, can be analyzed as long as it can be separated using case ID, activity, and timestamp attributes. In real-world scenarios, most data is not collected for process mining purposes or is unsuitable for use in process mining analyses. Especially data that is recorded manually or scattered among various isolated systems can contain errors. Despite the efforts made to improve the quality of input data in the mining process, it is still necessary to develop efficient frameworks and methods to identify, evaluate, and address data quality challenges in real business processes, which are often characterized by high volume and complexity. The results of this research can offer a fresh perspective for researchers, data science specialists, and business analysts. [ABSTRACT FROM AUTHOR]

In today's digital age, log files are crucial. However, the conversion of text log files into images has only recently been developed. The security of log files is a major source of concern, and the security of the systems in which the logs are stored determines the safety of the log file in process mining. This calls for the first conversion of a text log file into an image file. Thus, this research aims to convert the log files into images in a mugshot database and detect illegal activity and criminals from the converted images employing a novel Convolutional Neural Network (CNN). The developed model has three stages: pre-processing, feature extraction, and detection and matching. The pre-processing was performed by min-max normalization, and in feature extraction, the deep learning method was used. Moreover, in the detection phase, CNN is employed for detecting illegal activities, and the matching process is performed for detecting illegal activities from converted images and criminals in the mugshot database. The model's performance is evaluated in terms of precision, F1-score, recall, and accuracy values of 99.6%, 98.5%, 98.7%, and 99.8%, respectively. A further comparison has been performed to show the effectiveness of the suggested model over other methods. [ABSTRACT FROM AUTHOR]

Consumers are increasingly demanding with respect to the quality of the product they purchase, which at a business level means improving processes by adopting methodologies suitable for the management of information generated within the activities, which generates a large volume of data that is not easy to handle manually. Therefore, process mining takes a key role in the management of data in an optimal way, applied for automatic auditing of hazards and critical control points. This quality system, better known by its acronym HACCP, is a powerful tool to ensure product safety, hygiene and traceability. Normally, the analysis of the records is carried out at the auditor's discretion and by sampling due to the volume of data. For the management of this BIG DATA, there is process mining, a young discipline related to machine learning, data mining, modeling and process analysis. The present work aims to propose a method to apply process mining to improve the quality of automatic auditing of HACCP records in tuna companies. This research uses a qualitative methodology, with a survey of experts and research processes to collect information contributing to the importance of process mining in automatic auditing systems. A method is proposed to make process mining applicable for the automatic auditing of records on hazards and critical control points, which was validated with a case where all transactions were automatically analyzed. [ABSTRACT FROM AUTHOR]

The present investigation describes a methodology of hazards and critical control points applying Process Mining, where the information was managed in order to optimize resources. Its objective is to apply Process Mining for the automatic discovery of the titling management workflow, allowing to improve process management by extracting knowledge from events registered in the information system, the Process Mining tools provided help, identifying events and showing in an animated way the processes that are executed, managing to restrict unwanted situations that congest the flow of information, in order to obtain an analysis of the data, models, observe data frequency and time. The methodology used was analytical and synthetic with a quantitative approach under experimentation using Celonis, Disco and Pro-M tools, they helped with the necessary resources for the good performance of process mining, as a result it was possible to obtain a reduction of events in hidden routines, as well as a greater flow of information by processes and the reduction of time, improving the effectiveness of event control and allowing to manage the flow of information by processes of degree management in the Information Technology Career. [ABSTRACT FROM AUTHOR]

In the consistency detection of business process discovery research, existing methods of multi-perspective alignment between event logs and process models can only obtain the optimal alignment of one trace with the process model at a time. Meanwhile the computation of heuristic function in obtaining the optimal alignment is complex, leading to low computation efficiency. To solve above problems, the paper proposed a multi-perspective alignment between batch traces of event log and process model based on trace minimum edit distance. Firstly, the study selected multiple traces in the event log to form batch traces, and used a process mining algorithm to obtain the log model of the batch traces. Then it obtained the product model of log model and process model and their transition system, which was the search space of batch traces. Then it designed a heuristic function based on the minimum edit distance between Petri net transition’s sequence set and the remaining traces to speed up the A* algorithm. Finally, the study designed a multi-perspective cost function that could adjust the weight of data and resource perspectives, and proposed a method of multi-perspective optimal alignment between each trace in the batch traces and the process model with the transition system of the product model. Compared with existing work, the simulation results show the proposed method takes up less memory space and uses less running time. This method improves the computation speed of heuristic function for optimal alignment, and obtains all optimal alignments of batch traces at one time, thus improves the efficiency of multi-perspective alignment between event logs and process models. [ABSTRACT FROM AUTHOR]

Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants' recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations. [ABSTRACT FROM AUTHOR]

This study explored the sequential interplay between challenges and regulatory processes in high- and low-performing collaborative groups. 66 students from a Finnish higher education institution participated in a collaborative task in groups of three. Approximately 34 h of video data were coded. The sequential analysis revealed that both groups had higher sequential transitions between cognitive regulation and emotional/motivational regulation, rather than cognitive challenges. The high-performing groups demonstrated a stronger sequential link between emotional/motivational regulation and cognitive regulation than the low-performing groups did when faced with cognitive challenges. The study establishes a theoretical grounding and advances our understanding of regulated learning. Since collaborative learning tasks are highly adopted in the higher education context, especially in the Nordic region, this study has practical implications for higher education in these countries and beyond as they seek to develop pedagogical methodologies and customised support to help collaborative groups resolve challenges by initiating regulatory processes. [ABSTRACT FROM AUTHOR]

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a "three-dimensional" view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. [ABSTRACT FROM AUTHOR]

Due to the complex and dangerous nature of underground industrial sites, there is an urgent need to improve practical teaching abilities. In this paper, a VR-based interactive teaching and practice environment is proposed for teaching mining equipment processes under laboratory conditions. This environment integrates a digitised design, a virtual simulation platform, and a VR interactive product operation. This VR interactive environment includes the structural cognition of mining equipment, the actual operating conditions, and the actual operation of electronic control. Moreover, this system integrates a series of virtual reality human–machine interaction hardware, so that the students can quickly learn the operating conditions and the states of the actual underground production. This system helps them comprehensively learn the mechanical, electrical, communication, control, and mining methods involved in the mining equipment. Finally, this system provides a virtual practice path for training innovative engineering technicians. [ABSTRACT FROM AUTHOR]

The ideology behind process modelling is to visualise lengthy event logs into simple representations interpretable to the end user. Classifying process models as simple or complex is based on criteria that evaluate attributes of models and quantify them on a scale. These metrics measure various characteristics of process models and describe their qualities. Over the years, vast amounts of metrics have been proposed in the community, making it difficult to find and select the appropriate ones for implementation. This paper presents a state-of-the-art meta-review that lists and summarises all the evaluation metrics proposed to date. We have studied the behaviour of the four most widely used metrics in process mining with an experiment. Further, we have used seven healthcare domain datasets of varying natures to analyse the behaviour of these metrics under different threshold conditions. Our work aims to propose and demonstrate the capabilities to use our selected metrics as a standard of measurement for the process mining domain. [ABSTRACT FROM AUTHOR]

Because of the complexity of geological conditions and disturbance of external factors, the process of instability of open-pit landslides is very complicated. To illustrate this complex process, taking an open-pit coal mine landslide as an example, an accurate landslide geological section was established via detailed field investigation, remote sensing image interpretation, and field borehole data, in this study. A two-dimensional discrete-element model was established by the Fast CPU Matrix Computing of Discrete-Element Method (MatDEM, Version 1.60). The failure evolution of the landslide was investigated; then, the deformation behavior and dynamic characteristics of the landslide were systematically studied. The analyses of the failure process, velocity, displacement, and heat showed that the evolution of the landslide could be divided into the stages of initial deformation, accelerated deformation, and deceleration and stabilization. During the movement of the landslide, the gravitational potential energy of the landslide gradually converted into kinetic energy, heat, and elastic potential energy. The kinetic energy in the initial stage was small but rapidly increased and occupied the dominant position in the second stage. The kinetic energy gradually decreased and tended to be stable in the third stage. The final geometry and accumulation characteristics of the landslide obtained by numerical simulation were consistent with the corresponding field investigation and remote sensing interpretation. This work can provide a reference for the study of the evolution and prevention of open-pit mine landslides. In view of the observation that the open-pit slope is prone to catastrophic damage, taking the West Open-Pit Mine in Pingzhuang city, Inner Mongolia Autonomous Region, China, as an example, the discrete-element geological model of the slope is established to determine the relevant calculation parameters, and the deformation behavior and dynamic characteristics of the landslide are revealed with the help of the rich information (failure, velocity change, and energy change processes of the landslide) obtained from the simulation results. The geometric shape and accumulation characteristics simulated by the mesoscale discrete-element technique are in good agreement with the terrain features observed after the actual landslide. Through a comparison and analysis of the monitoring data from the monitoring points arranged on site, the limitations of monitoring feedback landslide information are determined. These findings provide a certain reference for the numerical simulation study of open-pit coal mine landslides with discrete elements. [ABSTRACT FROM AUTHOR]

The malware detection methods based on data flow graph only focused on the data flow information caused by API calls, which ignored the sequence information among API calls. To address this issue, this paper integrated the sequence information of API calls into data flow graph, put forward the concept of dual data flow graph combined with sequence information, gave the method of constructing event logs from the API call sequence, and proposed the mining method of dual data flow graph combined with sequence information. Finally, this paper proposed an improved graph convolution network to classify dual data flow graph combined with sequential information. \The classification result could be used to detect and classify malwares. The experimental results show that the proposed method has better detection accuracy than traditional methods based on data flow graphs. [ABSTRACT FROM AUTHOR]

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers' actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others. [ABSTRACT FROM AUTHOR]

PURPOSE: A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology. METHODS: We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports. RESULTS: The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P =.89). CONCLUSION: We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records. Our semiautomated pipeline captures real-world tumor evolution from retrospective PET-CT scans. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphism (SNP) is one of the most widely used molecular markers to help researchers understand the relationship between phenotypes and genotypes. SNP calling mainly consists of two steps, including read alignment and locus identification based on statistical models, and various software have been developed and applied in this issue. Meanwhile, in our study, very low agreement (<25%) was found among the prediction results generated by different software, which was much less consistent than expected. In order to obtain the optimal protocol of SNP mining in tree species, the algorithm principles of different alignment and SNP mining software were discussed in detail. And the prediction results were further validated based on in silico and experimental methods. In addition, hundreds of validated SNPs were provided along with some practical suggestions on program selection and accuracy improvement were provided, and we wish that these results could lay the foundation for the subsequent analysis of SNP mining. [ABSTRACT FROM AUTHOR]

A large number of residual ores in the intermediate pillars, crown, and sill pillars left by the shrinkage mining process adopted in the upper deposit of Chaihulanzi Gold Mine are highly economically valuable.To further improve the utilization rate of residual ore resources, this paper based on the existing scheme of the mine proposes 4 overall mining technology schemes for residual ore resources.Based on ABAQUS numerical simulation software, the corresponding numerical simulation model is established.The size striking direction extends 420 m, the vertical direction 616 m, and the height 160 m; the width of the surrounding rock is 50 m.The recovery rate, displacement field, and surface subsidence of different schemes were obtained, and the optimal technical extraction scheme was finally determined to be the horizontal and vertical striped evenly supported ore pillar extraction scheme through comparative analysis.The results show that in actual production, the scheme can maximize the economic benefits under the condition of safe mining while recovering the residual mineral resources under the non-cemented waste rock filling body. [ABSTRACT FROM AUTHOR]

The wear characteristics and related mechanisms of the Longwall Shearer Haulage System were investigated. Wear is one of the main reasons for failures and downtimes. This knowledge can help solve engineering problems. The research was carried out at a laboratory station and a test stand. The publication presents the results of tribological tests carried out in laboratory conditions. The aim research was to select the alloy intended for casting the toothed segments of the haulage system. The track wheel was made by the forging method using steel 20H2N4A. Haulage System was tested on the ground using a longwall shearer. Selected toothed segments were subjected to tests on this stand. The cooperation of the track wheel and toothed segments in the tootbar were analyzed by a 3D scanner. Debris chemical composition was also appointed, as well as mass loss of toothed segments. The developed solution toothed segment an increase in the service life of the track wheel in real conditions. The results of the research also contribute to reducing the operating costs of the mining process. [ABSTRACT FROM AUTHOR]

Traditional business process-extraction models mainly rely on structured data such as logs, which are difficult to apply to unstructured data such as images and videos, making it impossible to perform process extractions in many data scenarios. Moreover, the generated process model lacks analysis consistency of the process model, resulting in a single understanding of the process model. To solve these two problems, a method of extracting process models from videos and analyzing the consistency of process models is proposed. Video data are widely used to capture the actual performance of business operations and are key sources of business data. Video data preprocessing, action placement and recognition, predetermined models, and conformance verification are all included in a method for extracting a process model from videos and analyzing the consistency between the process model and the predefined model. Finally, the similarity was calculated using graph edit distances and adjacency relationships (GED_NAR). The experimental results showed that the process model mined from the video was better in line with how the business was actually carried out than the process model derived from the noisy process logs. [ABSTRACT FROM AUTHOR]

With the increase of mining depth, disaster degree of rock burst in fault-controlled area is becoming more and more serious. To analyze mechanism and control method of fault rock burst, No. 3316 mining face controlled by No. 3DF56 fault in Jining mine, China, is selected as case study. First, by analysis of general situation of the mining face, mechanical characteristics of coal rock, and micro-seismic events, it is determined that the coal seam has the possibility of rock burst in the process of the face mining towards the fault. Second, based on strength theory of rock, criterion of fault shear instability is obtained, and influence of cohesion and friction angle on the stability of fault is analyzed, and it is concluded that improving the cohesion of rock can control the fault slip. Third, by borehole detection recorder, it is determined that fracture areas of fault surrounding rock distribute in the range of 0–4 m from the surface of roadway surrounding rock and 0–5.0 m from the fault. Finally, control method of "precise grouting to couple shallow and deep boreholes" is introduced, and the grouting borehole scheme is optimized. Field test results show that the method can improve the compressive strength of coal rock and distribution laws of micro-seismic events, weaken the possibility of fault slip, effectively control the deformation of roadway surrounding rock and improve the control effect of rock burst. It can provide a reference for other coal mines with similar geological conditions. [ABSTRACT FROM AUTHOR]

This work studies the problem of clustering one-dimensional data points such that they are evenly distributed over a given number of low variance clusters. One application is the visualization of data on choropleth maps or on business process models, but without over-emphasizing outliers. This enables the detection and differentiation of smaller clusters. The problem is tackled based on a heuristic algorithm called DDCAL (1d distribution cluster algorithm) that is based on iterative feature scaling which generates stable results of clusters. The effectiveness of the DDCAL algorithm is shown based on 5 artificial data sets with different distributions and 4 real-world data sets reflecting different use cases. Moreover, the results from DDCAL, by using these data sets, are compared to 11 existing clustering algorithms. The application of the DDCAL algorithm is illustrated through the visualization of pandemic and population data on choropleth maps as well as process mining results on process models. [ABSTRACT FROM AUTHOR]

Aim: The aim was to discover longitudinal trajectories and patterns of events preceding adolescent‐to‐mother family violence in a geographic locale in Australia. Design: This was a retrospective case series. Methods: Routinely collected administrative data were sourced and linked from police and health service electronic records for adolescents born between 1994 and 2006 who had been issued a legal action for a family violence‐related offence (n = 775). A time‐stamped log of events from birth (where available) was created. Process mining was employed to discover dominant events and trajectories in the log from birth until adolescents' first recorded offence against their mother. Results/Findings Most adolescents in the case series offended against mothers (63%, n = 486). Trajectory analysis confirmed dominant early childhood events were repeated exposure to parental intimate partner violence (P‐IPV), parental drug and/or alcohol use and neglect. During early adolescence, pathways towards adolescent‐to‐mother violence involved other offending, drug and/or alcohol use and mental health service contact. Conclusion: The trajectories evidenced provide a complex picture of the emergence of adolescent‐to‐mother violence. From an early intervention perspective, it was found that many children and mothers were identifiable from police records in early childhood, at an average age of 35 months. Responses to adolescent family violence need to acknowledge the impact of childhood trauma and emerging mental health problems, along with strategies to mitigate the effect of P‐IPV on mother‐to‐child relationships. Impact This is the first large‐scale study to specifically examine trajectories from birth for adolescents who engage in violence towards mothers. The findings have important implications for the design and delivery of early intervention childhood services and interagency collaboration in nursing and midwifery services. In early adolescence, contact with mental health services represents an opportunity for screening and support interventions. This is an important preventive timepoint for family violence, adolescent drug and alcohol use and other offences. [ABSTRACT FROM AUTHOR]

Process models, e.g., BPMN models, may represent how companies in an ecosystem interact with each other. However, the business model of the same ecosystem, e.g., expressed by an e 3 v a l u e model, is often left implicit. This hinders the proper analysis of the ecosystem at the business level, and more specifically financial assessment, for which process models are less appropriate. Therefore, the question is if we can somehow derive e 3 v a l u e models from BPMN models. This would not only allow for proper business model analysis but would also facilitate business model mining, similar to the success of process mining. However, although an e 3 v a l u e model and BPMN model represent the same ecosystem, their perspectives differ significantly. Therefore an automated derivation of an e 3 v a l u e model from a BPMN seems not to be feasible, but we can assist the e 3 v a l u e model designer with practical guidelines. We explore and test our guidelines in two real-world settings, we then analyze and evaluate its application to better understand their limitations and how to improve them. [ABSTRACT FROM AUTHOR]

Purpose: New maintenance hypotheses such as lean smart maintenance emphasized internal integration. Since the maintenance process is not fully integrated with other business processes, it indicates that some of the problems in the maintenance process are caused by other departments. Additionally, nothing can be managed or improved without first measuring it. In order to enhance internal integration, this study developed a model that makes use of information systems data to examine synchronization and collaboration across departments engaged in maintenance operations. Design/methodology/approach: This research connects maintenance management and business process management through information systems. A conceptual module model based on CMMS is proposed that will use data which are already available in CMMS and, using process mining, will assess the level of synchronization between departments within an organization. Findings: This conceptual model will serve as a roadmap for creating better value-added CMMS software. This system operates as a performance measurement tool in three majors, including organizational analysis, workflow analysis and eventually, a future simulation of maintenance processes. This module will serve as a decision support system, highlighting opportunities for improvement in maintenance processes. Originality/value: A practical guideline is provided for the future development of CMMSs and their enhancement to intelligence. All assumptions are based on maintenance theories, techniques for measuring maintenance performance and business process management and process mining. [ABSTRACT FROM AUTHOR]

The problem of high stress conditions is a critical challenge for mining activity in deep metal mines, and the difficulty and economic cost of maintaining stope stability also increases. In order to ensure stope stability in the mining process, it is necessary to evaluate stope stability for a more stable stope structure. However, there are many kinds of methods for rock stability assessment, and most of them are based on empirical and analytical methods, which lead to uncertain results. In this paper, a comprehensive evaluation method including factors of stope stability, self-stabilization time, maximum safety span, and support measure is determined, which is more likely to provide a comprehensive result. Finally, it was verified by the field application in Jiaojia Gold Mine. Results show that it can accurately evaluate the stope stability from different aspects compared with the traditional single evaluation method, and it leads to a more comprehensive and reliable result. [ABSTRACT FROM AUTHOR]